• Journal of Information Technology Services
• /
• v.14 no.3
• /
• pp.49-65
• /
• 2015

As South Korean large business groups, also known as Chaebol, have broadened their influence in the domestic economy, it is important to analyze the influence of internal transactions among Chaebol affiliates on their performance. In this paper, relationship between internal transactions and efficiency of Chaebol affiliates has been analyzed. Top five Chaebol groups in South Korea are selected; they include Samsung, Hyundai Motors, LG, SK, and Lotte group. Based on internal transactions among affiliates, social networks are constructed for each Chaebol group to analyze centrality, network structures and cliques. Data Envelopment Analysis (DEA) was conducted to examine the efficiency of the Chaebol affiliates. Then, correlations between the degree centrality and the efficiency of Chaebol affiliates were analyzed, and the network structures of Chaebol groups are presented. The result shows that positive correlations between degree centrality and efficiency are observed among four Chaebol Groups. This paper shows that the Social Network Analysis (SNA) techniques can be used in the empirical research for the analysis of internal transactions of Chaebol groups.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.709-711
• /
• 2010

This paper discusses a design and performance analysis of control network on large-scaled ship. Ship control network can be composed many actuator, sensors and controllers considering reliability and real-time performance. SMS(Ship Message Specification) is based on real time Ethernet is proposed for ship control networks. Considering ship environment, the proposed scheme is investigated through computer simulation.

• Journal of information and communication convergence engineering
• /
• v.16 no.2
• /
• pp.84-92
• /
• 2018

For large wireless sensor networks running on battery power, the time synchronization of all sensor nodes is becoming a crucial task for waking up sensor nodes with exact timing and controlling transmission and reception timing. However, as network size increases, this synchronization process tends to require long processing time consume significant power. Furthermore, a naïve synchronization scheduler may leave some nodes unsynchronized. This paper proposes a power-efficient scheduling algorithm for time synchronization utilizing the notion of density, which is defined by the number of neighboring nodes within wireless range. The proposed scheduling algorithm elects a sequence of minimal reference nodes that can complete the synchronization with the smallest possible number of hops and lowest possible power consumption. Additionally, it ensures coverage of all sensor nodes utilizing a two-pass synchronization scheduling process. We implemented the proposed synchronization algorithm in a network simulator. Extensive simulation results demonstrate that the proposed algorithm can reduce the power consumption required for the periodic synchronization process by up to 40% for large sensor networks compared to a simplistic multi-hop synchronization method.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.53-62
• /
• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Korean Journal of Artificial Intelligence
• /
• v.11 no.2
• /
• pp.1-6
• /
• 2023

This paper presents a comprehensive scheme for assessing the importance of multiple microgrids (MGs) network that includes distributed energy resources (DERs), renewable energy systems (RESs), and energy storage system (ESS) facilities. Due to the uncertainty of severe weather, large-scale cascading failures are inevitable in energy networks. making the assessment of the structural vulnerability of the energy network an attractive research theme. This attention has led to the identification of the importance of measuring energy nodes. In multiple MG networks, the energy nodes are regarded as one MG. This paper presents a modified PageRank algorithm to assess the importance of MGs that include multiple DERs and ESS. With the importance rank order list of the multiple MG networks, the core MG (or node) of power production and consumption can be identified. Identifying such an MG is useful in preventing cascading failures by distributing the concentration on the core node, while increasing the effective link connection of the energy flow and energy trade. This scheme can be applied to identify the most profitable MG in the energy trade market so that the deployment operation of the MG connection can be decided to increase the effectiveness of energy usages. By identifying the important MG nodes in the network, it can help improve the resilience and robustness of the power grid system against large-scale cascading failures and other unexpected events. The proposed algorithm can point out which MG node is important in the MGs power grid network and thus, it could prevent the cascading failure by distributing the important MG node's role to other MG nodes.

• 전기의세계
• /
• v.30 no.5
• /
• pp.297-305
• /
• 1981

The problem of finding shortest paths between every pair of points in a network is solved employing and optimal network decomposition in which the network is decomposed into a number of subnetworks minimizing the number of cut-set between them while each subnetwork is constrained by a size limit. Shortest path computations are performed on individual subnetworks, and the solutions are recomposed to obtain the solution of the original network. The method when applied to large scale networks significantly reduces core requirement and computation time. This is demonstrated by developing a computer program based on the method and applying it to 30-vertex, 160-vertex, and 273-vertex networks.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.96.2-96
• /
• 2001

This paper presents a control method for the experimental mobile vehicle. By merging the advantages of neural network, adaptive and fuzzy control, neural network-based adaptive fuzzy control is proposed. It can deal with a large amount of training data by neural network, from these data producing more accurate fuzzy rules by adaptive control, and then controlling the object by fuzzy control. This is not the simple combination of the three methods, but merging them into one control system Experiments and some future considerations are given.

• International Journal of Reliability and Applications
• /
• v.9 no.1
• /
• pp.1-5
• /
• 2008

A heuristic algorithm for reliability optimization of a distributed network system is developed so that the reliability of a large system can be determined efficiently. This heuristic bases on the determination of maximal reliability set of maximum node capacity, maximal link reliability and maximal node degree.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.41 no.1
• /
• pp.1-8
• /
• 1992

Hopfield neural network has been applied to the problem of economic load dispatch(ELD) of electric power. The optimum values of neuron potentials are represented in terms of large numbers. The differential synchronous transition mode is used in this simulation. Through case studies, we have shown the possibility of the application of neural network to ELD. In case of including the transmission losses, the proposed method has an advantage that the problem can be solved simply with one neural network, without calculating incremental fuel costs and incremental losses required by traditional method.

• Journal of Advanced Information Technology and Convergence
• /
• v.8 no.2
• /
• pp.119-127
• /
• 2018

Training a very large deep neural network can be painfully slow and prone to overfitting. Many researches have done for overcoming the problem. In this paper, a combination of early stopping and ADAM based deep neural network was presented. This form of deep network is useful for handling the big data because it automatically stop the training before overfitting occurs. Also generalization ability is better than pure deep neural network model.